Farmers Perception of Climate Variability in Three Urban...

International Journal of Ecological Science and Environmental Engineering 2018; 5(2): 43-51

http://www.aascit.org/journal/ijesee

ISSN: 2375-3854

Keywords Climate Variability,

Knowledge,

Perceptions,

and Predictability

Received: October 29, 2017

Accepted: November 27, 2017

Published: January 25, 2018

Farmers Perception of Climate Variability in Three Urban Fringe Communities of Ilorin, Nigeria

Oyeniyi Solomon Taiwo

Department of Geography and Environment Management, Faculty of Social Sciences, University

of Ilorin, Ilorin, Nigeria

Email address [email protected]

Citation Oyeniyi Solomon Taiwo. Farmers Perception of Climate Variability in Three Urban Fringe

Communities of Ilorin, Nigeria. International Journal of Ecological Science and Environmental

Engineering. Vol. 5, No. 2, 2018, pp. 43-51.

Abstract Change in climate and consequent global warming are posing threats to food security in

many developing nations including Nigeria because of the climate-dependent nature of

agricultural systems and lack of coping capabilities. This paper assessed farmer

perceptions of climate variability in three urban fringe communities of Ilorin with a view

to understanding farmers’ knowledge, opinion and response as regards the issue. Using

systematic random sampling techniques, one hundred and fifty questionnaires were

administered on arable farmers in the study areas. A cross-sectional questionnaires were

administered, Focus Group Discussion (FGD) was used to appraise farmers’

predictability of rainfall as rainfall is the most important climatic variable in agricultural

production. It was observed that the farmers are aware of the changes in climate and

generally agreed that it was easy to predict the coming season and the seasons were

distinct but now the rains have become more and more unpredictable. The study found

that Climate variability has affected their crop yield and farm income. Rainfall, raining

days, maximum temperature, minimum temperature and average relative humidity, were

found to be significant determinants of crop outputs. Farmers had adopted some coping

strategies such as, planting of different varieties of crops, changing the expanse of land

put into crop production, use of chemical fertilizers, planting short gestation crops,

believing these will go a long way reducing the effect of climate variability.

1. Study Area

The study areas for this research are; Ganmo, Oyun and Shao communities (figure 1).

Ganmo is located on latitude 8°25’N and longitude 4°36’E, it is 11.33km from Ilorin,

Shao is located on latitude 8°35’N and longitude 4°33’E, it is 10.65km from Ilorin and

Oyun got its name from River Oyun located in the area [4]. The climate of Ilorin is

tropical under the influence of the two trade winds prevailing over the country hence two

climatic seasons i.e. rainy and dry season. The rainy season is between March and

November and the annual rainfall varies from 1000mm to 1500mm, with the peak

between September and early October. Also, the mean monthly temperature is generally

high throughout the year with 25°C in January, May 27.5°C and September 22.5°C

Ajibade, 2002 adapted from [8].

Ilorin is composed by ferruginous tropical soils on crystalline acid rocks. The

landscape consists of a relatively flat and undulating land with interspersed hills and

valleys in parts of Baruten, Kaiama and Moro Local Government areas [2]. Ilorin is

located in the transition zone between the deciduous forest (rainforest) of the southern

International Journal of Ecological Science and Environmental Engineering 2018; 5(2): 43-51 44

and the savannah grasslands of the north. The vegetation of

Ilorin composed of species of plants such as locust beans

trees, shear butter trees, elephant grasses, shrubs and

herbaceous plants among others are common in this area [3].

The population census of 1991 put the population of Ilorin at

532,088. The figure was projected with the annual growth

rate of 2.84 percent to be 606,533 in 1996 [5]. The 2006

census put the population at 766,000 (NPC, 2006).

Source: Generated from Map Library, 2013 [10]

Figure 1. Kwara State Showing the Sixteen Local Government Areas.

2. Results and Discussion

2.1. Personal Characteristics of Sampled

Arable Farmers

The personal characteristics of arable farmers like sex, age,

marital status, farming experience, size of household, size of

farmland, other occupation apart from farming and level of

education were discussed in this section.

Table 1 shows the percentage sex distribution of arable

farmers in the study areas. The result of analysis shows that

all the sampled 150 respondents were males, there were no

females. This shows the peculiarity of the study areas, where

the female gender is disallowed to engage in laborious work

and obligated to attend to milder activities like trading among

others. The age of the farmers revealed that majority (64%)

were above 60 years of age. This means that the arable

farmers sampled are relatively old. This goes to buttress the

fact that agriculture is seen as an occupation for the aged

while the young look for white-collar jobs in the urban areas.

All the farmers (100%) were married and this means more

mouth to be fed and possibility of getting family labour. In

respect to their farming experience, 94% have been farming

for over 20years. The table also shows that 12.67%, 49.33%,

and 38% of the respondents have household size of 4-6

persons, 7-9persons, and 10-12 persons respectively. The

mean household size stood at approximately 9 persons per

household in the study areas. Having large household size as

in this case is sometimes advantageous because it substitutes

for labour input.

Table 1 further revealed that 71.33% of the respondents

had farm size less than 2.00ha. The percentage distribution

according to the farmers engagement in off-farm job include;

artisans (59.33%), traders (2%), retired (4%) while the

remaining 34.67% did not have off-farm job as at the time of

carrying out this research (See Table 1). From the data

obtained from the field, larger proportion of the farmers had

no formal education and this constitutes 61.33% of the

respondents while the rest had formal education.

45 Oyeniyi Solomon Taiwo: Farmers Perception of Climate Variability in Three Urban Fringe Communities of Ilorin, Nigeria

Table 1. Personal Characteristics of Sampled Arable Farmers.

Characteristics Frequency Percentage

Frequency

Cumulative

Frequency

a. Sex

Male 150 100 100

Female - - 100

TOTAL 150

b. Age

30-40 5 3.33 3.33

41-50 16 10.67 14.00

51-60 33 22.00 36.00

Above 60 96 64 100

TOTAL 150

c. Marital Status

Single - - -

Married 150 100 100

Widowed - - 100

Divorced - - 100

Separated - - 100

TOTAL 150

d. Farming Experience

1-5 - - -

6-10 - - -

11-15 7 4.67 4.67

16-20 2 1.33 6.00

21+ 141 94.00 100

TOTAL 150

e. Size of Household

1-3 - - -

4-6 19 12.67 12.67

7-9 74 49.33 62.00

10-12 57 38.00 100

12+ - - 100

TOTAL 150

f. Size of Farmland

0.01-1.00ha 12 8 8.00

1.01-2.00ha 47 31.33 39.33

2.01-3.00ha 48 32 71.33

3.01-4.00ha 10 6.67 78.00

Above 4.01ha 33 22 100

TOTAL 150

g. Other Farm Occupation

Artisan 89 59.33 59.33

Trader 3 2.00 61.33

Civil Servant - - 61.33

Retired 6 4.00 65.33

Non 52 34.67 100

TOTAL 150

h. Educational Qualification

No Formal Education 92 61.33 61.33

Primary Education 41 27.33 88.66

Secondary Education 13 8.67 97.33

Tertiary Education 4 2.67 100

TOTAL 150

Source: Field Survey, 2013.

2.2. Farmers Perception of Climate

Variability

One of the objectives of this research is to appraise

farmers' perception of climate variability in three

communities in urban fringe of Ilorin. Table 2, 3 and 4 show

their perception about rainfall, temperature and length of

rainy season respectively.

Majority (78%) of the sampled farmers (Ganmo 16.67%,

Shao 29.33% and Oyun 32%) perceived that the amount of

rainfall is decreasing, 12.67% increasing, while the

remaining 9.33% don’t know whether it is increasing or

decreasing (See Table 2).

Table 2. Farmers Perception of Rainfall.

Farmer’s Perception Ganmo Shao Oyun Total

Increasing 13 (8.67) 6 (4) 0 (0) 19 (12.67)

Decreasing 25 (16.67) 44 (29.33) 48 (32) 117 (78)

I don’t know 12 (8) 0 (0) 2 (1.33) 14 (9.33)

Percentages are in parentheses


Table 3 shows that almost all the respondents in the study

areas (92%) perceived that temperature is increasing, 4.67%

perceived it is decreasing and 3.33% from Ganmo don’t

know whether it is increasing or decreasing.

Table 3. Farmers Perception of Temperature.


Increasing 45 (30) 43 (28.67) 50 (33.33) 138 (92)

Decreasing 0 (0) 7 (4.67) 0 (0) 7 (4.67)

I don’t know 5 (3.33) 0 (0) 0 (0) 5 (3.33)



The result of the analysis from table 4 indicates that

75.33% (Ganmo 18.67%, Shao 26% and Oyun 30.67%) of

the sampled farmers perceived that the length of rainy

seasons is decreasing. From the remaining 24.66%, 17.33%

perceived that there is no changes, increasing 4% and 3.33%

don’t know whether it is decreasing, increasing or there is no

changes.

Table 4. Farmers Perception of Length of Rainy Seasons.


Increasing 0 (0) 6 (4) 0 (0) 6 (4)

Decreasing 28 (18.67) 39 (26) 46 (30.67) 113 (75.33)

No changes 21 (14) 5 (3.33) 0 (0) 26 (17.33)

I don’t know 1 (0.67) 0 (0) 4 (2.67) 5 (3.33)



Summarily, it can be deduced from table 2, 3 and 4 that the

amount of rainfall and length of rainy seasons is decreasing,

while temperature is decreasing. This shows that the farmers

in the study areas not only perceived, but are also aware of

the variations in these key elements (i.e. rainfall and

temperature) of climate.

2.2.1. Farmers’ Predictability of Rainfall

Table 5 reveals farmers’ predictability of rainfall. Majority

(120 i.e. 80%) of the sampled farmers (Ganmo 26.67%, Shao

23.33% and Oyun 30%) generally agreed that it was easy to

predict the coming season and the seasons were distinct but

now the rains have become more and more unpredictable.


However 27 (18%) of the respondents believed that rainfall is

still predictable while 3 (2%) respondents believed that it is

highly predictable.

Table 5. Farmers’ Predictability of Rainfall.

Farmer’s Opinion Ganmo Shao Oyun Total

Highly predictable 3 (2) 0 (0) 0 (0) 3 (2)

Predictable 7 (4.67) 15 (10) 5 (3.33) 27 (18)

Unpredictable 40 (26.67) 35 (23.33) 45 (30) 120 (80)

Highly unpredictable 0 (0) 0 (0) 0 (0) 0 (0)



2.2.2. Result of the Focus Group Discussion

(FGDs)

According to [1], rainfall is the most important climatic

variable in agricultural production. In respect to this Focus

Group Discussion (FGD) was used to appraise farmers’

predictability of rainfall in term of its amount and the length

of rainy seasons.

One of the farmers in the Focus Group Discussion at

Ganmo admitted

“it is now difficult for us to predict the length of rainy

seasons (say between March and November)”.

Another farmer added,

“there is now higher incidence of dry spells, which have

also increased in intensity”.

In Shao one farmer in the Focus Group Discussion

highlighted,

“we are now experiencing shorter rainy seasons than before”.

Another man in the Focus Group Discussion at Oyun

remark,

“it is now difficult us to predict amount of rainfall in a

season”, he added “there are some trained personnel

(weather forecasters) who do predict the amount of rainfall

and its length”.

2.3. Causes of Climate Variability

This study further assessed farmers' perception about the

causes of climate variability as one of the objectives 76% of

the sampled farmers (Ganmo 26.67%, Shao 18% and Oyun

31.33%) perceived climate variability purely as God’s work.

They believed that God meted out punishment on human

beings because of our wicked ways. In addition, there is an

indication that farmers in Ganmo and Oyun seriously

disregard the role played by anthropogenic activities in the

increase of climate variability as 6% of the sampled farmers

in Shao believed that human beings are the cause of climate

variability (See Table 6).

Also from table 6, 12.67% of the sampled farmers (Ganmo

5.33%, Shao 5.33% and Oyun 2%) associated the cause of

climate variability to angers of gods. The remaining 5.33%

(Ganmo 1.33% and Shao 4%) believed that it is a natural

phenomenon. These natural causes were cited as natural

changes in winters, low or high temperatures and changes in

wind movement, among others.

Table 6. Causes of Climate Variability.

Causes Ganmo Shao Oyun Total

Natural phenomenon 2 (1.33) 6 (4) 0 (0) 8 (5.33)

Human activity 0 (0) 9 (6) 0 (0) 9 (6)

Angers of gods 8 (5.33) 8 (5.33) 3 (2) 19 (12.67)

God’s work 40 (26.67) 27 (18) 47 (31.33) 114 (76)



2.3.1. Effects of Climate Variability on Crop

Yield and Farm Income

This section assessed the effects of climate variability on crop

yield and farm income. The four major crops produce by the

sampled farmers are; Maize, Yam, Cassava and Cowpea. The

result of the analysis revealed that 82.67% of the sampled

farmers (Ganmo 18%, Shao 31.33% and Oyun 33.33%) are now

having low yield despite the fact that all other factors (amount of

land cultivated, type of crops grown, type of farming practices

etc.) remains constant (See Table 7). This decrease in crop yield

was associated to climate variability and this invariably leads to

lower income compared to some years back. In addition to this,

it can also lead to food scarcity and increase in prices of food.

Also from table 7, 15.33% (from Ganmo) admitted that they are

still having normal yield because climate variability is not really

affecting them, but rather the Fulani’s who use their cows to

graze on their farmland.

Table 7. Effect of Climate Variability on Crop Yield and Farm Income.

Effect Ganmo Shao Oyun Total

High 0 (0) 3 (2) 0 (0) 3 (2)

Low 27 (18) 47 (31.33) 50 (33.33) 124 (82.67)

Normal 23 (15.33) 0 (0) 0 (0) 23 (15.33)



2.3.2. Extreme Weather Event on Farm

The extreme weather events noticed by the sampled farmers

on their farm is analyzed in this section. The cause of these

noticed weather events - flooding, drought and destructive

winds is linked to climate variability. The result of the analysis

revealed that 48% of the sampled farmers (Ganmo 10.67%,

Shao 16.67% and Oyun 20.67%) had experienced drought on

their farm, 15 farmers in Shao (this accounted for 10% of the

total sampled farmers) experienced destructive winds breaking

their cowpea at flowering stage and invariably reducing their

crop yield and farm income. 4.67% (Ganmo 2% and Shao

2.67) experienced flooding, while the remaining 29.33% have

not noticed anything of such (See Table 8).

Table 8. Extreme Weather Event on Farm.

Weather event Ganmo Shao Oyun Total

Flooding 3 (2) 4 (2.67) 0 (0) 7 (4.67)

Drought 16 (10.67) 25 (16.67) 31 (20.67) 72 (48)

Destructive winds 0 (0) 15 (10) 0 (0) 15 (10)

I don’t know 31 (20.67) 6 (4) 9 (6) 44 (29.33)




2.4. Agricultural Extension Officers and

Improved Agricultural Techniques

Agricultural extension officers are intermediaries between

research and farmers. They operate as facilitators and

communicators, helping farmers in their decision making and

ensuring that appropriate knowledge is implemented to obtain

the best result. Agricultural extension officers need to

communicate to farmers’ agricultural information on crops, on

how best to utilize the farmland, how to adapt to climate

variability, etc. Each agricultural extension officer is linked to

one of the agricultural development centres for example

Agricultural Development Project Board, Ministry of

Agriculture, International Institute of Tropical Agriculture, etc.

Larger percentage of the sampled farmers 58% especially in

Ganmo (28%) and Oyun (26%) Agricultural Extension

Officers have not been to their community, not to say that they

will train them on improved agricultural techniques to adapt to

climate variability. 28.67% (Ganmo 2.67%, Shao 18.67% and

Oyun 7.33%) admitted that they have been trained once and

11.33% two times majorly on fertilizer application – technique

of application, time to apply, type of fertilizer that suite each

crops etc. to obtain the best result (See Table 9).

Table 9. Agricultural Extension Officers and Improved Agricultural

Techniques.

No of times Ganmo Shao Oyun Total

0 42 (28) 6 (4) 39 (26) 87 (58)

1 4 (2.67) 28 (18.67) 11 (7.33) 43 (28.67)

2 4 (2.67) 13 (8.67) 0 (0) 17 (11.33)

3 0 (0) 3 (2) 0 (0) 3 (2)



2.5. Improved Agricultural Techniques

Table 10 shows that 19.33% of the total sampled farmers

(Ganmo 2.67%, Shao 18% and Oyun 4.67%) have been

trained on fertilizer application as a means of adapting to

climate variability. It is believed that fertilizer will haste plant

growth and increase its productivity if properly applied.

78.67% (Ganmo 30%, Shao 20% and Oyun 28.67%)

professed that Agricultural Extension Officers have not

trained them any improved agricultural techniques to adapt to

climate variability. Some of those farmers added that

Agricultural Extension Officers have not been to their

community before.

Table 10. Agricultural Techniques.

Improved Agricultural

Techniques Ganmo Shao Oyun Total

No technique 45 (30) 30 (20) 43 (28.67) 118 (78.67)

Application of fertilizers 4 (2.67) 18 (12) 7 (4.67) 29 (19.33)

Planting different

varieties of crops 1 (0.67) 0 (0) 0 (0) 1 (0.67)

Planting improved

seedlings 0 (0) 2 (1.33) 0 (0) 2 (1.33)



2.6. Coping Strategies to Climate Variability

Climate variability and its impacts have led communities

to develop coping strategies such as crop rotation, mulching,

increase hectares of land cultivated among others. These

coping strategies have been passed from generation to

generation through traditional and cultural practices.

However these could be improved by agricultural extension

officers disseminating current knowledge on adaptation

methods to them.

Figure 3 demonstrates strategies adopted by the sampled

farmers to cushion the effect of climate variability in their

area. 28% of the total sampled farmers adopted planting of

different varieties of crops, 7% changed the expanse of land

put into crop production and 8% used chemical fertilizers

because of the believe that it will go a long way in reducing

the effect of climate variability. 5% adopted planting short

gestation crops, 7% adopted different planting date, while

18% no adaptation method because some of them lack

current knowledge of adapting to climate variability

(because of their low level of education) and those that are

informed about the modern techniques of coping with

climate variability lack the money to acquire these

techniques. The remaining 27% believed that nothing can

be done by human beings than to pray to God for

favourable seasons, they lamented about the rate at which

temperature is increasing nowadays and the significant

reduction in the amount of rainfall and length of raining

seasons.

Figure 2. Coping Strategies to Climate Variability.

2.7. Pattern of Climatic Variables and Crop

Yield for a Period of Ten Years

(2002-2011)

This subsection examines the data obtained from Kwara

State Agricultural Development Board (KWADP) on

pattern of climatic variables and crop yield for 2002 -

2011. Line graphs was used to shows the variation in the

climatic variables – rainfall, number of raining days,

minimum temperature, maximum temperature and average

relative humidity. Production yield of maize, yam, cassava

and cowpea under the years reviewed was also

represented.


Table 11. Climatic Data for 2002 – 2011.

Year Rainfall (mm) No of raining days Temperature (°C) (max) Temperature (°C) (min) AVG. Relative Humidity (%)

2002 1028.50 66 36.44 20.30 77.00

2003 811.75 50 31.17 17.50 83.00

2004 1597.40 56 33.33 20.15 82.00

2005 1144.50 55 35.90 23.90 82.50

2006 1236.99 78 36.47 22.79 81.00

2007 1481.63 78 37.08 22.50 78.60

2008 1381.90 60 36.00 22.00 84.00

2009 1526.57 72 38.00 23.40 87.10

2010 1165.70 62 36.00 23.30 87.40

2011 1253.40 59 36.10 22.91 84.42

Source: Kwara State Agricultural Development Project (KWADP)[9]

Table 11 shows the climatic data over a period of ten years while Figures 3 and 4 show the trends of these climatic variables.

According to [1], rainfall is the most important climatic variable in agricultural production so rainfall graph was plotted separately.

Figure 3. Line graph for climatic variables.

Figure 3 reveals that there are variations in the climatic

variables tested, but that of raining days is more glaring.

2003 has the minimum number (50) of raining days while

2006 and 2007 has the highest, it falls in 2008 (60) and

increased again in 2009 (72). The trends of minimum and

maximum temperature are somehow stable between 2004

and 2011. Average relative humidity experienced slight

variation between 2003 and 2007 and rise from 2008 to 2010.

Figure 4. Line graph for rainfall.


The amount of rainfall from 2002 to 2011 varies from

811.5mm to 1597.40mm. It dropped from 1028.50mm in

2002 to 811.5mm in 2003 (the lowest in the period), it

reached its peak 2004 (1597.40mm) and starts to fluctuate

from 2005 (1144.50mm), 2008 (1381.90mm), 2010

(1165.70mm) and 2011 (1253.40mm) See Figure 4.

Table 12 reveals that there are variations in the yield of the

sampled crops, while Figure 5 shows the trend of the yield.

Cassava which is the leading crop has the highest yield in the

year 2008 (17.14), decreased in 2009 (15.97), increase

steadily in 2010 and 2011. Yam has its highest output in 2011

(16.80) and lowest output in 2004 (12.21), while there is no

much variation in the output of maize and cowpea. The

changes were attributed to variations in the climate of the

study area. These are illustrated in figure 5.

Table 12. Crop Production Yield (Tons/Ha) in ’000 (2002 – 2011).

Year Maize Yam Cassava Cowpea

2002 1.30 12.33 12.94 0.14

2003 1.47 10.86 12.56 0.17

2004 1.25 11.70 12.21 0.13

2005 1.35 11.63 12.46 0.25

2006 1.58 11.85 15.28 0.26

2007 1.37 11.66 16.99 0.44

2008 1.43 12.46 17.14 0.40

2009 1.50 12.46 15.97 0.45

2010 1.47 12.53 16.48 0.43

2011 1.49 13.14 16.80 0.46

Source: Kwara State Agricultural Development Project (KWADP)[9]

Figure 5. Line graph showing crop yield (2002 - 2011).

2.8. Multiple Regression Analysis for Crop

Yield and Climatic Variables

Multiple Regression analysis was employed to determine

the percentage contribution of each of the climatic variables

to crop yield.

The regression equation is: Y = a + b1x1 + b2x2 + b3x3 +

b4x4 + b5x5 + b6x6 + e

Where Y = Crop yield, X1 = Rainfall, X2 = Raining Days,

X3 = Maximum Temperature, X4 = Minimum Temperature, X5

= Relative Humidity, X6 = Number of raining days, e = error

term, a = intercept i.e. the value of ‘y’ when x1x2 - - - - - xn are

zero b1b2 - - - - - bn = gradient of the multiple regression line. Correlation analysis is use to assess the relationship

between climatic data and crop yield. The correlation

coefficient analysis (Table 13) employed for the study reveals

that maximum temperature is positively and highly correlated

with yam (0.640) and cassava (0.610), minimum temperature

is highly and positively correlated with yam (0.572) and

cassava (0.558). This means that increase in maximum and

minimum temperature will of the study area may lead to a

higher yield for yam and cassava. Number of raining days is

positively correlated with cassava (0.515), this means that

increase in the number of raining days may lead to a higher

yield for cassava. Relatively humidity is correlated with

maize, this shows that there is a strong positive correlation

between relative humidity and maize yield i.e. increase in

relative humidity of the study area may lead to a higher yield

for maize.

Other climatic variables are positively but weakly

correlated with the crops under study except rainfall and

maize (-0.187) and cowpea (-0.636) which are negatively

correlated. This means that the higher the rainfall, the lower

the yield of maize and cowpea i.e. excessive rainfall is not

good for maize and cowpea. Raining days and cowpea (-

0.328), maximum temperature and cowpea (-0.328) and

minimum temperature and cowpea (-0.363) are negatively

correlated. This means that the more the raining days and the

higher the maximum and minimum temperature, the lower

the yield of cowpea i.e. raining days, maximum and

minimum temperature are not good for cowpea.


Table 13. Correlation Analysis of Climatic Variables and Crops.

Crop Rainfall (mm) Raining Days Temperature (max) Temperature (min) AVG Relative Humidity

Maize -0.187 0.310 0.199 0.278 0.515

Yam 0.302 0.193 0.640 0.572 0.350

Cassava 0.380 0.515 0.610 0.558 0.357

Cowpea -0.636 -0.328 -0.328 -0.363 0.134

Source: Researchers’ computation. Correlation is significant at the 0.05 level (2-tailed).

The regression analysis computed for the crops revealed

that maize, yam, cassava and cowpea have coefficient of

determination of Table 0.80, 0.66, 0.55 and 0.52 respectively.

This means that 80, 66, 55 and 52% of the variance in maize,

yam, cassava and cowpea can be respectively explained by

the climatic parameters investigated (Table 14). The

implication of this is that 20, 34, 45 and 48% of the variance

in maize, yam, cassava and cowpea can be respectively

explained by other factors such amount of land cultivated,

type of farming practices, soil fertility, etc. This is in support

of [6] and [7] findings.

Table 14. Regression Analysis.

Crop R R2 Standard Error F P-Value

Maize 0.892 0.796 68.78785 3.118 0.147

Yam 0.813 0.661 562.59044 1.563 0.343

Cassava 0.744 0.554 2094.01789 0.992 0.518

Cowpea 0.721 0.520 180.80840 0.867 0.571

Source: Researchers’ computation.

3. Summary

The study found a positive relationship between rainfall,

raining days, maximum temperature, minimum temperature

and average relative humidity and output of yam and cassava.

Maize is inversely related to rainfall, but has positive

relationship with other elements of climate aforementioned

while cowpea is inversely related to rainfall, raining days,

maximum temperature and minimum temperature but

positively related with average relative humidity. However,

the sampled climatic elements; rainfall, raining days,

maximum temperature, minimum temperature and average

relative humidity, were found to be significant determinants

of crop outputs.

It was also observed that climate variability and its impacts

have led communities to develop coping strategies such as

farmers planting of different varieties of crops, changing the

expanse of land put into crop production, use of chemical

fertilizers, planting short gestation crops etc. because of the

believe that it will go a long way in reducing the effect of

climate variability.

4. Conclusion

Climate variability has been seen to have significant effect

on crop production based on farmers’ perception. This is

because their agricultural yield has decreased from what it

used to be some ten years ago. This has also affected their

income because agriculture is climate dependent. The effect

of which is more pronounced whenever there are variations

in these climatic elements – rainfall, raining days, minimum

temperature, maximum temperature and average relative

humidity. It can therefore be concluded that the effect of

climate variability can be reduced if farmers are been

educated on the causes and current methods of adaptation.

Recommendations

The knowledge and information gap concerning the causes

of climate variability, effect, information dissemination,

awareness programmes and training programmes calls for

immediate action. Therefore, the following recommendations

are made based on the findings of the study:

a) Farmer should be more enlightened about the causes of

climate variability, most especially on the human

induced ones such as - industrial and agricultural

practices including animal husbandry, forest and

grassland clearing and burning, lumbering, fuel wood

and charcoal extraction, oil extraction, burning of fossil

fuel, etc.

b) Policies must aim at promoting farm-level adaptation

through emphasis on the early warning systems and

disaster risk management and also, effective

participation of farmers in adopting better agricultural

and land use practices.

c) There is an urgent need for meteorological reports and

alerts to be made accessible when necessary to farmers

in an understandable form.

d) Massive campaign on the reality of climate variability,

its impacts on food crop production and modern

adaptation measures is highly recommended. This could

be achieved by organizing seminars on climate

variability regularly for them.

e) Extension services should be more improved in the

study area. This is with the aim of educating the farmers

on the suitable coping strategies on climate variability.

References

[1] Ayoade, J. O. (2004). Introduction to Climatology for the Tropics. Spectrum Books Limited, Ibadan pp 258.

[2] Ifabiyi I. P and Omoyosoye O. (2011). Rainfall Characteristics and Maize Yield in Kwara State, Nigeria. Indian Journal of Fundamental and Applied Life Sciences ISSN: 2231-6345 (Online) http://www.cibtech.org/jls.htm 2011 1 (3) July-September, pp. 60-65.


[3] Jimoh H. I and Ajao Lukman (2009). Problems of Suspended Sediments Loads in Asa River Catchment, Ilorin, Nigeria. Pakistan Journal of Social Sciences Year: 2009, 6 (1) 19-25.

[4] Microsoft Encarta (2009). "Ilorin." Microsoft Encarta 2009 [DVD]. Redmond, WA: Microsoft Corporation, 2008.

[5] National Population Commission (1991). Census News Publication, 3 (1), Lagos Nigeria.

[6] Olanrewaju, R. M. (2010). The Impact of Climate on Yam Production in Kwara State, Nigeria. Environmental Issues, 3 (1): 30-34.

[7] Tunde A. M., Usman B. A. and Olawepo V. O. (2011). Effects of climatic variables on crop production in Patigi L. G. A., Kwara State, Nigeria. Journal of Geography and Regional Planning 4 (14) 695-700, 18 November, 2011.

[8] Tunde, A. M., Adeleke, E. A. and Adeniyi, E. E. (2013). Impact of Climate Variability on Human Health in Ilorin, Nigeria. Environment and Natural Resources Research 3 (1).

[9] KWADP (2010). Kwara State Agricultural Development Projects. Annual Reports. Kwara State Government.

[10] Map library (2013). http://www.maplibrary.org

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